#Social Networks #Dunbar number

¿Cuánta gente vas a acabar conociendo en toda tu vida?

Article (in Spanish) in the spanish newspaper El País about how many people we will know in our life. This is a summary of our recent research on how humans create/destroy relationships and how narrow and small is our world even throughout a life of encounters, relationships, work, etc. ¿Cuánta gente vas a acabar conociendo en toda tu vida? Muy poca En el mundo hay más de 7.500 millones de personas. ...

#social networks #Human Dynamics #Big Data

The dynamic character of our networked society

We live in a networked society and our actions, opinions, behaviors are affected and can affect other people. Understanding such social networked structures is one of the key challenges in our attempt to decode human behavior and its impact in our society. Although human interactions are dynamical by nature, most of our understanding relies in static representations of those social networks. However, social interactions are rarely static. Very often the networks evolve by means of processes that happen at diverse time scales, like link decay/formation, group formation, etc. ...

#Twitter #R #Social Networks

Growing old in Twitter

I started using Twitter more than 10 years ago (!). I open an account in this social network in 2008 and although I was not using it too much for the first year, I become a frequent user after that. It has helped me to get news, information both for my personal and professional interests. But not only that, Twitter has been also the data source for our research, that helped us to investigate the relationship between human behavior in the social platform and paramount problems in our society as information propagation, unemployment, disaster damage, political opinion. ...

#Social Networks #Prediction algorithms #Privacy

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

Authors: Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P Michalak, Talal Rahwan Journal: Preprint (2018). arXiv Abstract: Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. ...

#Temporal Networks #Social Networks #Mobile Phone Data

Temporal patterns behind the strength of persistent ties

Authors: Henry Navarro, Giovanna Miritello, Arturo Canales, Esteban Moro Journal: EPJ Data Science (2017) 6:31 LINK Abstract: Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. ...